LLM-based User Profile Management for Recommender System

• URL: http://arxiv.org/abs/2502.14541v1
• Date: Thu, 20 Feb 2025 13:20:19 GMT
• Title: LLM-based User Profile Management for Recommender System
• Authors: Seunghwan Bang, Hwanjun Song,
• Abstract summary: PURE builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. We introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods.
• Score: 15.854727020186408
• License:
• Abstract: The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.

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